From A to E: Analyzing TPC’s OLTP Benchmarks -- The obsolete, the ubiquitous, the unexplored

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The isolation level Multiversion Read Committed (RC), offered by many database systems, is known to trade consistency for increased transaction throughput. Sometimes, transaction workloads can be safely executed under RC obtaining the perfect isolation of ...

Non-Volatile Memory (NVM) is an emerging type of memory device that provides fast, byte-addressable, and high-capacity durable storage. NVM sits on the memory bus and allows durable data structures designs similar to the in-memory equivalent ones. Expensiv ...

High-Level Synthesis (HLS) tools generate hardware designs from high-level programming languages. These tools almost universally build datapaths that are controlled using a centralized controller which relies on a static, compile-time schedule to determine ...

Micro-architectural behavior of traditional disk-based online transaction processing (OLTP) systems has been investigated extensively over the past couple of decades. Results show that traditional OLTP systems mostly under-utilize the available micro-archi ...

The growing demand for data-intensive decision support and the migration to multi-tenant infrastructures put databases under the stress of high analytical query load. The requirement for high throughput contradicts the traditional design of query-at-a-time ...

The use of punching shear reinforcement is currently considered as one of the most convenient practices to enhance the punching shear strength and deformation capacity of slab-column connections in flat slabs. Intensive research has been performed in the l ...

Recent years have witnessed a rise in real-world data captured with rich structural information that can be better depicted by multi-relational or heterogeneous graphs. However, research on relational representation learning has so far mostly focused on th ...

In this paper, we address the problem of privacy-preserving training and evaluation of neural networks in an N-party, federated learning setting. We propose a novel system, POSEIDON, the first of its kind in the regime of privacy-preserving neural network ...

Modern large-scale data platforms manage colossal amount of data, generated by the ever-increasing number of concurrent users. Geo-replicated and sharded key-value data stores play a central role when building such platforms. As the strongest consistency m ...

For decades mathematical modeling in epidemiology has helped understanding the dynamics of infectious diseases, as well as describe possible intervention scenarios to prevent and control them. However, such models were relying on several assumptions, such ...

From A to E: Analyzing TPC’s OLTP Benchmarks -- The obsolete, the ubiquitous, the unexplored

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Robustness against Read Committed for Transaction Templates

Software Support for Non-Volatile Memory (NVM) Programming

High-Level Synthesis of Dynamically Scheduled Circuits

Micro-architectural analysis of in-memory OLTP: Revisited

Scalable Multi-Query Execution using Reinforcement Learning

A mechanical approach for the maximum punching resistance of shear-reinforced slab-column connections

Representation Learning for Multi-relational Data

POSEIDON: Privacy-Preserving Federated Neural Network Learning

Efficient Protocols for Enforcing Causal Consistency in Geo-Replicated Key-Value Data Stores

Contact networks across ages: computational analysis of epidemiological dynamics